Instructions to use google-bert/bert-base-german-cased with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use google-bert/bert-base-german-cased with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="google-bert/bert-base-german-cased")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("google-bert/bert-base-german-cased") model = AutoModelForMaskedLM.from_pretrained("google-bert/bert-base-german-cased", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from google-bert/bert-base-german-cased: direct link, hf CLI and curl.
- Browser
- Download file 436 MB
-
https://huggingface.co/google-bert/bert-base-german-cased/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://google-bert/bert-base-german-cased/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/google-bert/bert-base-german-cased/resolve/main/flax_model.msgpack
436 MB
- Xet hash:
- ca07495b93fd5017553d56536693ba72b9fe745763fef696f98c8aa8a2e3b7da
- Size of remote file:
- 436 MB
- SHA256:
- fb127d4c33e1a89ab189883c405ad620a0e084144c7af743ff6bd4f530492812
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